Plot Types¶

This notebook demonstrates all the plot types available in xarray_plotly.

In [1]:

import plotly.express as px
import xarray as xr

from xarray_plotly import config, xpx

config.notebook()  # Configure Plotly for notebook rendering

import plotly.express as px import xarray as xr from xarray_plotly import config, xpx config.notebook() # Configure Plotly for notebook rendering

Load Sample Data¶

We'll use plotly's built-in datasets converted to xarray:

• stocks: Tech company stock prices over time
• gapminder: Country statistics (life expectancy, GDP, population) by year

In [2]:

# Stock prices: 2D (date, company)
df_stocks = px.data.stocks().set_index("date")
df_stocks.index = df_stocks.index.astype("datetime64[ns]")

stocks = xr.DataArray(
    df_stocks.values,
    dims=["date", "company"],
    coords={"date": df_stocks.index, "company": df_stocks.columns.tolist()},
    name="price",
)
print(f"stocks: {dict(stocks.sizes)}")

# Stock prices: 2D (date, company) df_stocks = px.data.stocks().set_index("date") df_stocks.index = df_stocks.index.astype("datetime64[ns]") stocks = xr.DataArray( df_stocks.values, dims=["date", "company"], coords={"date": df_stocks.index, "company": df_stocks.columns.tolist()}, name="price", ) print(f"stocks: {dict(stocks.sizes)}")

stocks: {'date': 105, 'company': 6}

In [3]:

# Gapminder: pivot to create multi-dimensional arrays
df_gap = px.data.gapminder()

# Life expectancy: 2D (year, country) - select a few countries
countries = ["United States", "China", "Germany", "Brazil", "Nigeria"]
df_life = df_gap[df_gap["country"].isin(countries)].pivot(
    index="year", columns="country", values="lifeExp"
)

life_exp = xr.DataArray(
    df_life.values,
    dims=["year", "country"],
    coords={"year": df_life.index, "country": df_life.columns.tolist()},
    name="life_expectancy",
    attrs={"units": "years"},
)
print(f"life_exp: {dict(life_exp.sizes)}")

# Gapminder: pivot to create multi-dimensional arrays df_gap = px.data.gapminder() # Life expectancy: 2D (year, country) - select a few countries countries = ["United States", "China", "Germany", "Brazil", "Nigeria"] df_life = df_gap[df_gap["country"].isin(countries)].pivot( index="year", columns="country", values="lifeExp" ) life_exp = xr.DataArray( df_life.values, dims=["year", "country"], coords={"year": df_life.index, "country": df_life.columns.tolist()}, name="life_expectancy", attrs={"units": "years"}, ) print(f"life_exp: {dict(life_exp.sizes)}")

life_exp: {'year': 12, 'country': 5}

In [4]:

# GDP per capita by continent and year (aggregated)
df_continent = df_gap.groupby(["continent", "year"])["gdpPercap"].mean().reset_index()
df_gdp = df_continent.pivot(index="year", columns="continent", values="gdpPercap")

gdp = xr.DataArray(
    df_gdp.values,
    dims=["year", "continent"],
    coords={"year": df_gdp.index, "continent": df_gdp.columns.tolist()},
    name="gdp_per_capita",
    attrs={"units": "USD"},
)
print(f"gdp: {dict(gdp.sizes)}")

# GDP per capita by continent and year (aggregated) df_continent = df_gap.groupby(["continent", "year"])["gdpPercap"].mean().reset_index() df_gdp = df_continent.pivot(index="year", columns="continent", values="gdpPercap") gdp = xr.DataArray( df_gdp.values, dims=["year", "continent"], coords={"year": df_gdp.index, "continent": df_gdp.columns.tolist()}, name="gdp_per_capita", attrs={"units": "USD"}, ) print(f"gdp: {dict(gdp.sizes)}")

gdp: {'year': 12, 'continent': 5}

Line Plot¶

Best for time series and continuous data:

In [5]:

fig = xpx(stocks).line(title="Stock Prices Over Time")
fig

fig = xpx(stocks).line(title="Stock Prices Over Time") fig

In [6]:

fig = xpx(life_exp).line(title="Life Expectancy by Country", markers=True)
fig

fig = xpx(life_exp).line(title="Life Expectancy by Country", markers=True) fig

Bar Chart¶

Best for comparing categorical data:

In [7]:

# GDP by continent for selected years
fig = xpx(gdp.sel(year=[1952, 1977, 2007])).bar(title="GDP per Capita by Continent")
fig

# GDP by continent for selected years fig = xpx(gdp.sel(year=[1952, 1977, 2007])).bar(title="GDP per Capita by Continent") fig

Grouped bars¶

In [8]:

fig = xpx(gdp.sel(year=[1952, 1977, 2007])).bar(barmode="group", title="GDP per Capita (Grouped)")
fig

fig = xpx(gdp.sel(year=[1952, 1977, 2007])).bar(barmode="group", title="GDP per Capita (Grouped)") fig

Area Chart¶

Best for showing composition over time:

In [9]:

# Population by continent over time
df_pop = df_gap.groupby(["continent", "year"])["pop"].sum().reset_index()
df_pop_pivot = df_pop.pivot(index="year", columns="continent", values="pop")

population = xr.DataArray(
    df_pop_pivot.values / 1e9,  # Convert to billions
    dims=["year", "continent"],
    coords={"year": df_pop_pivot.index, "continent": df_pop_pivot.columns.tolist()},
    name="population",
    attrs={"units": "billions"},
)

fig = xpx(population).area(title="World Population by Continent")
fig

# Population by continent over time df_pop = df_gap.groupby(["continent", "year"])["pop"].sum().reset_index() df_pop_pivot = df_pop.pivot(index="year", columns="continent", values="pop") population = xr.DataArray( df_pop_pivot.values / 1e9, # Convert to billions dims=["year", "continent"], coords={"year": df_pop_pivot.index, "continent": df_pop_pivot.columns.tolist()}, name="population", attrs={"units": "billions"}, ) fig = xpx(population).area(title="World Population by Continent") fig

Scatter Plot¶

Best for showing relationships between variables:

In [10]:

fig = xpx(stocks).scatter(title="Stock Prices Scatter")
fig

fig = xpx(stocks).scatter(title="Stock Prices Scatter") fig

Box Plot¶

Best for showing distributions:

In [11]:

# Stock price distributions by company
fig = xpx(stocks).box(title="Stock Price Distributions")
fig

# Stock price distributions by company fig = xpx(stocks).box(title="Stock Price Distributions") fig

In [12]:

# Life expectancy distributions by country
fig = xpx(life_exp).box(title="Life Expectancy Distribution by Country")
fig

# Life expectancy distributions by country fig = xpx(life_exp).box(title="Life Expectancy Distribution by Country") fig

Heatmap (imshow)¶

Best for 2D grid data:

In [13]:

fig = xpx(life_exp).imshow(title="Life Expectancy Heatmap")
fig

fig = xpx(life_exp).imshow(title="Life Expectancy Heatmap") fig

With different colorscale¶

In [14]:

fig = xpx(gdp).imshow(
    color_continuous_scale="Viridis",
    title="GDP per Capita by Continent and Year",
)
fig

fig = xpx(gdp).imshow( color_continuous_scale="Viridis", title="GDP per Capita by Continent and Year", ) fig

Faceting¶

All plot types support faceting to create subplot grids:

In [15]:

# Create 3D data: life expectancy by year, country, and metric
# We'll add GDP as another "metric" dimension
df_metrics = df_gap[df_gap["country"].isin(countries)].pivot(
    index="year", columns="country", values="gdpPercap"
)
gdp_countries = xr.DataArray(
    df_metrics.values,
    dims=["year", "country"],
    coords={"year": df_metrics.index, "country": df_metrics.columns.tolist()},
    name="gdp_per_capita",
)

# Combine into 3D array
combined = xr.concat(
    [life_exp, gdp_countries / 1000],  # Scale GDP to thousands
    dim=xr.Variable("metric", ["Life Exp (years)", "GDP (thousands USD)"]),
)

fig = xpx(combined).line(
    facet_col="metric",
    title="Country Comparison: Life Expectancy and GDP",
)
fig

# Create 3D data: life expectancy by year, country, and metric # We'll add GDP as another "metric" dimension df_metrics = df_gap[df_gap["country"].isin(countries)].pivot( index="year", columns="country", values="gdpPercap" ) gdp_countries = xr.DataArray( df_metrics.values, dims=["year", "country"], coords={"year": df_metrics.index, "country": df_metrics.columns.tolist()}, name="gdp_per_capita", ) # Combine into 3D array combined = xr.concat( [life_exp, gdp_countries / 1000], # Scale GDP to thousands dim=xr.Variable("metric", ["Life Exp (years)", "GDP (thousands USD)"]), ) fig = xpx(combined).line( facet_col="metric", title="Country Comparison: Life Expectancy and GDP", ) fig

Animation¶

Create animated plots by assigning a dimension to animation_frame:

In [16]:

# Animated bar chart of GDP by continent over time
fig = xpx(gdp).bar(
    x="continent",
    animation_frame="year",
    title="GDP per Capita by Continent (Animated)",
    range_y=[0, 35000],
)
fig

# Animated bar chart of GDP by continent over time fig = xpx(gdp).bar( x="continent", animation_frame="year", title="GDP per Capita by Continent (Animated)", range_y=[0, 35000], ) fig

In [17]:

# Animated line showing life expectancy evolution
fig = xpx(life_exp).bar(
    x="country",
    animation_frame="year",
    title="Life Expectancy by Country (Animated)",
    range_y=[0, 85],
)
fig

# Animated line showing life expectancy evolution fig = xpx(life_exp).bar( x="country", animation_frame="year", title="Life Expectancy by Country (Animated)", range_y=[0, 85], ) fig